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J Mol Graph Model. 2001;20(3):199-218.

Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach.

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Department of Computational Science, National University of Singapore.


Determination of potential drug toxicity and side effect in early stages of drug development is important in reducing the cost and time of drug discovery. In this work, we explore a computer method for predicting potential toxicity and side effect protein targets of a small molecule. A ligand-protein inverse docking approach is used for computer-automated search of a protein cavity database to identify protein targets. This database is developed from protein 3D structures in the protein data bank (PDB). Docking is conducted by a procedure involving multiple conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Potential protein targets are selected by evaluation of molecular mechanics energy and, while applicable, further analysis of its binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Our results on several drugs show that 83% of the experimentally known toxicity and side effect targets for these drugs are predicted. The computer search successfully predicted 38 and missed five experimentally confirmed or implicated protein targets with available structure and in which binding involves no covalent bond. There are additional 30 predicted targets yet to be validated experimentally. Application of this computer approach can potentially facilitate the prediction of toxicity and side effect of a drug or drug lead.

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